Comparison of resting electroencephalogram coherence in patients with mild cognitive impairment and normal elderly subjects

Hadiyoso, Sugondo; Wijayanto, Inung; Aulia, Suci

doi:10.11591/ijece.v12i2.pp1558-1564

Cited by 3 publications

(4 citation statements)

References 36 publications

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“…Coherence measurements in the Alzheimer’s dataset have been reported in a previous study [ 38 ]. The coherence calculations results show that the MCI group’s coherence is lower than the normal elderly subjects.…”

Section: Resultsmentioning

confidence: 99%

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Hadiyoso

Zakaria

Ong

et al. 2023

Sensors

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Dementia is a term that represents a set of symptoms that affect the ability of the brain’s cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer’s is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient’s quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients.

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Section: Resultsmentioning

confidence: 99%

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Hadiyoso

Zakaria

Ong

et al. 2023

Sensors

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“…Comparison of coherence changes in the control and chiropractic groups shows that the inter-hemispheric coherence changes in all frequency bands are significantly different between the two groups. Research has shown that EEG inter-hemispheric coherence is significantly lower in patients with AD and MCI than in controls ( Zheng-yan, 2005 ; Hadiyoso et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…Intra-hemispheric coherence analysis showed a significant reduction in the connection of the frontal-temporal-central-occipital network, which was found significantly in the pair of electrodes containing FP1. Decreased intrahemispheric coherence in MCI patients is related to the disconnection of cortico-cortical connectivity which connects the temporoparietal, and occipital areas with the frontal areas ( Hadiyoso et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Investigating the effects of chiropractic care on resting-state EEG of MCI patients

Ziloochi,

Niazi,

Amjad

et al. 2024

Front. Aging Neurosci.

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IntroductionMild cognitive impairment (MCI) is a stage between health and dementia, with various symptoms including memory, language, and visuospatial impairment. Chiropractic, a manual therapy that seeks to improve the function of the body and spine, has been shown to affect sensorimotor processing, multimodal sensory processing, and mental processing tasks.MethodsIn this paper, the effect of chiropractic intervention on Electroencephalogram (EEG) signals in patients with mild cognitive impairment was investigated. EEG signals from two groups of patients with mild cognitive impairment (n = 13 people in each group) were recorded pre- and post-control and chiropractic intervention. A comparison of relative power was done with the support vector machine (SVM) method and non-parametric cluster-based permutation test showing the two groups could be separately identified with high accuracy.ResultsThe highest accuracy was obtained in beta2 (25–35 Hz) and theta (4–8 Hz) bands. A comparison of different brain areas with the SVM method showed that the intervention had a greater effect on frontal areas. Also, interhemispheric coherence in all regions increased significantly after the intervention. The results of the Wilcoxon test showed that intrahemispheric coherence changes in frontal-occipital, frontal-temporal and right temporal-occipital regions were significantly different in two groups.DiscussionComparison of the results obtained from chiropractic intervention and previous studies shows that chiropractic intervention can have a positive effect on MCI disease and using this method may slow down the progression of mild cognitive impairment to Alzheimer’s disease.

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“…This real dataset was created to aid researchers working on EEG motion artifact reduction techniques by allowing them to compare and contrast existing methods as well as develop new models. Hadiyoso et al [15] presented a study comparing the coherence of EEG signals in mild cognitive impairment (MCI) patients and healthy people. Its goal is to make each group's feature visible so that the coherence value can be used to detect early Alzheimer's disease via EEG analysis.…”

Section: Electroencephalography Signalmentioning

confidence: 99%

A survey on bio-signal analysis for human-robot interaction

Radha

Hassan

2022

IJECE

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<span lang="EN-US">The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems.</span>

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Comparison of resting electroencephalogram coherence in patients with mild cognitive impairment and normal elderly subjects

Cited by 3 publications

References 36 publications

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Multi Modal Feature Extraction for Classification of Vascular Dementia in Post-Stroke Patients Based on EEG Signal

Investigating the effects of chiropractic care on resting-state EEG of MCI patients

A survey on bio-signal analysis for human-robot interaction

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